Remote People Data Analyst: Shape People Ops with Data

Enfuce; United States; Hybrid; USD 80,000 - 120,000; Full time

Benefits offered by this job

Flexible, remote work

Stock options

5 weeks PTO + family leave

Team events & activities

Healthcare benefits

Job summary

Enfuce is seeking a Data Analyst to transform messy people data into clean, structured information and deliver insights that guide People Team decisions. You will partner with the central Data & AI group to build a scalable data platform and later support AI-powered People products.

You will begin with foundation work, ensuring data quality across systems, then expand to feeding data into agents and automation. Collaboration with People Team and central teams is essential.

Qualifications

• 3+ years of experience as a Data Analyst within a business function.
• Strong SQL skills and experience with cloud data platforms (Snowflake preferred).
• Hands-on experience with dbt or similar analytics engineering practices.
• Comfort with Git-based development workflows.
• Experience with BI/analytics platforms (ThoughtSpot preferred; Looker/Omni also).
• Experience with sensitive data and analytics access controls.

Responsibilities

• Build a data foundation you can trust and move reporting to ThoughtSpot with central Data & AI team.
• Turn data into direction by analyzing friction and engagement drivers.
• Connect people data to Finance for sharper headcount planning & forecasting.
• Prove what we build works by setting success measures and verifying outcomes.
• Power AI builds by shaping data layers for agents and automation.

Skills

SQL

dbt

Git workflows

BI platforms

ThoughtSpot

Tools

Snowflake

BigQuery

Databricks

Looker

Omni

Job description

Enfuce is seeking a Data Analyst to transform messy people data into clean, structured information and deliver insights that guide People Team decisions. You will partner with the central Data & AI group to build a scalable data platform and later support AI-powered People products.

You will begin with foundation work, ensuring data quality across systems, then expand to feeding data into agents and automation. Collaboration with People Team and central teams is essential.